# -*- coding: utf-8 -*-
"""CLUTRR_Dataset Loading Script.ipynb

Automatically generated by Colaboratory.

Original file is located at
    https://colab.research.google.com/drive/1q9DdeHA5JbgTHkH6kfZe_KWHQOwHZA97
"""

# coding=utf-8
# Copyright 2019 The CLUTRR Datasets Authors and the HuggingFace Datasets Authors.
#
# CLUTRR is CC-BY-NC 4.0 (Attr Non-Commercial Inter.) licensed, as found in the LICENSE file.
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

# Lint as: python3
"""The CLUTRR (Compositional Language Understanding and Text-based Relational Reasoning) benchmark."""


import csv
import os
import textwrap

import numpy as np

import datasets


_CLUTRR_CITATION = """\
@article{sinha2019clutrr,
  Author = {Koustuv Sinha and Shagun Sodhani and Jin Dong and Joelle Pineau and William L. Hamilton},
  Title = {CLUTRR: A Diagnostic Benchmark for Inductive Reasoning from Text},
  Year = {2019},
  journal = {Empirical Methods of Natural Language Processing (EMNLP)},
  arxiv = {1908.06177}
}
"""

_CLUTRR_DESCRIPTION = """\
CLUTRR (Compositional Language Understanding and Text-based Relational Reasoning),
 a diagnostic benchmark suite, is first introduced in (https://arxiv.org/abs/1908.06177) 
 to test the systematic generalization and inductive reasoning capabilities of NLU systems.

"""
_URL = "https://github.com/kliang5/CLUTRR_huggingface_dataset/tree/main/"
_TASK = ["gen_train23_test2to10", "gen_train234_test2to10", "rob_train_clean_23_test_all_23", "rob_train_disc_23_test_all_23", "rob_train_irr_23_test_all_23","rob_train_sup_23_test_all_23"]

class CLUTRR_v1(datasets.GeneratorBasedBuilder):
    """BuilderConfig for CLUTRR."""

    BUILDER_CONFIGS = [
        datasets.BuilderConfig(
            name=task,
            version=datasets.Version("1.0.0"),
            description="",
        )
        for task in _TASK
    ]

    def _info(self):
        return datasets.DatasetInfo(
            description=_CLUTRR_DESCRIPTION,
            features=datasets.Features(
                {
                    "id": datasets.Value("string"),
                    "story": datasets.Value("string"),
                    "query": datasets.Value("string"),
                    "target": datasets.Value("string"),
                    "clean_story": datasets.Value("string"),
                    "proof_state": datasets.Value("string"),
                    "f_comb": datasets.Value("string"),
                    "task_name": datasets.Value("string"),
                    "story_edges": datasets.Value("string"),
                    "edge_types": datasets.Value("string"),
                    "query_edge": datasets.Value("string"),
                    "genders": datasets.Value("string"),
                    "task_split": datasets.Value("string"),
                }
            ),
            # No default supervised_keys (as we have to pass both premise
            # and hypothesis as input).
            supervised_keys=None,
            homepage="https://www.cs.mcgill.ca/~ksinha4/clutrr/",
            citation=_CLUTRR_CITATION,
        )

    def _split_generators(self, dl_manager):
          """Returns SplitGenerators."""
          # dl_manager is a datasets.download.DownloadManager that can be used to
          # download and extract URLs

          task = str(self.config.name)
          urls_to_download = {
              "test": _URL + task + "/test.csv",
              "train": _URL + task + "/train.csv",
              "validation": _URL + task + "/validation.csv",
          }
          downloaded_files = dl_manager.download_and_extract(urls_to_download)

          return [
              datasets.SplitGenerator(
                  name=datasets.Split.TRAIN,
                  # These kwargs will be passed to _generate_examples
                  gen_kwargs={
                      "filepath": os.path.join(downloaded_files["train"], task + "_train.jsonl"),
                      "task": task,
                  },
              ),
              datasets.SplitGenerator(
                  name=datasets.Split.VALIDATION,
                  # These kwargs will be passed to _generate_examples
                  gen_kwargs={
                      "filepath": os.path.join(downloaded_files["validation"], task + "_val.jsonl"),
                      "task": task,
                  },
              ),
              datasets.SplitGenerator(
                  name=datasets.Split.TEST,
                  # These kwargs will be passed to _generate_examples
                  gen_kwargs={
                      "filepath": os.path.join(downloaded_files["test"], task + "_test.jsonl"),
                      "task": task,
                  },
              ),
          ]

    def _generate_examples(self, filepath, task):
      """Yields examples."""
      with open(filepath, encoding="utf-8") as f:
        i = 0
        for line in f:
            data = json.loads(line)
            i += 1
            yield i, {
                "id": data["id"],
                "story": data["story"],
                "query": data["query"],
                "target": data["target"],
                "clean_story": data["clean_story"],
                "proof_state": data["proof_state"],
                "f_comb": data["f_comb"],
                "task_name": data["task_name"],
                "story_edges": data["story_edges"],
                "edge_types": data["edge_types"],
                "query_edge": data["query_edge"],
                "genders": data["genders"],
                "task_split": data["task_split"],
            }